This invention relates in general to processing of data in computer systems, and more specifically to a system for detecting and interpreting transactions, events or changes in data in computer systems.
There is great commercial value to businesses in being able to detect significant events or transactions, from thousands or hundreds of thousands daily, and in interpreting their significance in real-time or near real-time. Knowledge that one particular customer order was smaller than usual, can lead to a much greater likelihood of the business missing its revenue targets, and leads to a whole chain of effects throughout the supply chain. Timely knowledge of the significant of this particular event buys the business time to replace the missing value from the smaller order with orders from other customers, and/or slow manufacturing processes to fit the new likely demand, and defer reordering supplies and components.
The traditional approach to managing business performance is to compile data from different computer systems into a computer database often called a data warehouse. This is a repository for information about the business, and typically stores large numbers of facts about the business. Business analysts then use analysis software to sift through the data in order to understand business performance.
Most data warehouses today are updated on a weekly basis, with some updated daily, and in general are not designed for analyzing real-time data. Most data warehouse systems are also not designed to detect what has changed from previously, or to highlight to the business users significant transactions or events which could affect the business' ability to achieve its performance targets. As a result, most business performance analysis today is done manually—but this process is time consuming and a skilled task leading to a time delay in producing the analysis. This time lag between the transaction or event happening and being able to take action on the analysis is measured in weeks or months at many companies.
In most businesses today there is considerable pressure on management to have greater visibility into the future performance of the business. The ad hoc nature of today's manual analysis process also leads to a lack of consistency in how the data should be interpreted. Rarely are corrective actions, identified by one department of the business, taken in isolation of other departments. Other departments may be responsible for pieces of the business process, and to may even have performed their own analysis, and reached different conclusions as to the appropriate actions that need to be taken. These types of disagreements between departments, or managers, typically cause the remedy for the problem to be delayed while agreement is reached. Today, these negotiations and discussions typically happen in a way completely disconnected from the data and the analysis, and may involve email and other forms of communication. Thus it is highly desirable to provide a system which links these two processes together.
There is great value in business today, due to shorter business cycles, greater competition, and pressure from the investor community to identify problems and implement corrective actions as rapidly as possible. Therefore there is great value in speeding the correct interpretation of the data, and in speeding the collective understanding of the problem across departments within a business.